This lesson is still being designed and assembled (Pre-Alpha version)

Understanding Open and Reproducible Science

This is a carpentries lesson for bachelor and master students of all disciplines working at least in part empirically using the free software environment for statistical computing and graphics R This course aims to provide students with the basic why, what and how of Open and Reproducible Science, it is divided in six topics which are each discussed within a single episode:

  1. Introduction to Open science and its relation to scientific integrity and reproducible research
  2. Practical guidelines on how to organize data and projects in view of reproducibility
  3. Definition of quality criteria for good research when critically appraising publications and a discussion how these criteria are related to transparency and reproducible research
  4. Introduction to tools for scientific collaboration
  5. Introduction to reproducible notebooks for data analysis
  6. Implementation of the principles of Open Science and reproducible research when visualizing data

The course originates from a course given at the University of Zurich, Switzerland. Some of the provided links hence point to University of Zurich resources, which are then to be understood as an example for resources available at most universities.


This course is for Students of all disciplines which are working at least in part empirically and who have had an introduction to empirical research. Intermediate IT skills are a prerequisite: students need to know the file tree structure on their device (where is a file?) and need to be able to install packages and programs.

Participants need to have basic R skills, that is they need to know

  • how to assign values to an object,
  • how to manipulate and extract the entries of an object,
  • how to do simple calculations on objects such as percentages,
  • how to use functions such as t.test and
  • how to create simple plots.

Participants who do not have these skills can work through the first three lessons of “R for Social Scientists” at


Setup Download files required for the lesson
00:00 1. Scientific integrity, Open Science and reproducibility What is Scientific integrity and what is the link to Open Science and reproducibility?
What is Open Science and which aspects are important to me?
What is reproducibility and why should I care about it?
02:00 2. First steps towards more reproducibility Is there a reproducibility/replicability crisis?
How do I organize projects and software code to favor reproducibility?
How do I handle data in spreadsheets to favor reproducibility?
04:30 3. Facilitating reproducibility in academic publications How does academic publishing work?
What is the IMRAD format?
What are reporting guidelines and why are they useful for reproducibility?
How can we judge the quality and credibility of a preprint
07:00 4. Collaboration drives Open Science and is a challenge for reproducibility Why is collaborative work especially important for Open and Reproducible Science?
What are tools that faciliate collaborative work?
08:45 5. Reproducible notebooks for data analysis Should I use a graphical user interface to analyse data or a code-based system?
What is literate programming and what is R Markdown?
How do I use R Markdown
11:45 6. Reproducible and honest visualizations How to create graphs reproducibly?
How to transmit information truthfully in graphs?
What are the good practice principles for visualizations?
14:45 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.